The maneuvering target tracking problem - dynamic model

Abstract:

There is a growing need to enhance situation awareness in the maritime environment
utilizing new and current technologies. There are numerous ways to enhance situation
awareness by employing long-range vision detection systems, data fusion techniques, such
as combining radar and automatic identification system (AIS) data and data mining
techniques that allow for filtering out anomalies. With the proliferation of high-quality video
equipment and cheaper and faster computational machines, there is an increasing need for
automated video surveillance as the amount of information available to the operator for
processing is overwhelming. It is therefore necessary that only crucial information that may
negatively impact mission effectiveness be presented to the operator.
Whilst performing surveillance one would be interested in monitoring other surface vessels
within the sensor coverage. The detection and tracking of small and slow moving targets
having low signal-to-noise ratios is of interest in the maritime environment. This is
particularly challenging as influences from the natural environment, such as sea states, glint,
whitecaps and clutter, on a target is captured during image acquisition and this has adverse
effects on the tracking of a target.
A grey-scale based target tracking algorithm using the particle filter framework was
developed and tested in MATLAB® (R2008a). The main focus of the work is on the use of
dynamic models in a particle filtering framework. The dynamic model contributes to the
propagation of the particles in a particle filtering framework of the target grey-scale
distribution. The dynamic models investigated are the constant velocity model and an
acceleration model. The algorithm was tested with real-world image sequences in the
maritime environment. The targets were tracked for the duration of the image sequence and
the dynamic model that accounted for acceleration yielded better results when analysing the
position error between the estimated position and the ground truth data points. A slight
improvement in this error makes a significant difference on tracking a target as targets in the
maritime environment context are small. The future scope of the work would then include
accounting for more features of the target such as edge cues and/or implementing adaptive
observation models to improve the accuracy, stability and robustness of the algorithm for
real-time applications.